Moving data from Salesforce Marketing Cloud to HubSpot - what's the optimal approach?

[Grace_31Dance]

Hello everyone! I’m currently planning a transition from Salesforce Marketing Cloud to HubSpot and could use some guidance. Our current SFMC setup includes several different business units all operating under a single account structure.

I’m wondering if the built-in integration between SFMC and HubSpot can properly manage this multi-business unit configuration. Are there any specific challenges or pitfalls I should watch out for during this process?

My migration plan involves transferring all contact and company records, plus setting up a few custom fields in the new system. Since we’re dealing with more than one million contact records, I want to make sure I approach this carefully and avoid any potential data loss or corruption issues.

Has anyone here dealt with a similar large-scale migration? What would you recommend as the best strategy for handling this volume of data while maintaining data integrity throughout the transfer process?

[priya.in.pixels]

The Problem:

You are migrating a large volume (over one million records) of contact and company data from Salesforce Marketing Cloud (SFMC) to HubSpot, and you’re concerned about the complexities of handling multiple business units within a single SFMC account and ensuring data integrity during this large-scale migration. The built-in SFMC to HubSpot integration may not be sufficient for this level of complexity.

Understanding the “Why” (The Root Cause):

Migrating large datasets between CRM systems is inherently complex, and the challenges are amplified when dealing with multiple business units under a single account structure in the source system (SFMC). Direct integration tools often struggle with the volume and the need for precise data mapping across potentially numerous custom fields. Rate limits imposed by the APIs involved can further slow down and complicate the process. Data inconsistencies or mapping errors can lead to data loss or corruption, rendering the migration incomplete or inaccurate. Therefore, a carefully planned and automated approach is crucial.

Step-by-Step Guide:

This guide outlines a strategy for a robust, automated migration from SFMC to HubSpot, minimizing the risks associated with data volume and complex business unit structures. This approach prioritizes data integrity and control throughout the migration process.

Step 1: Data Assessment and Planning:

1. Detailed Field Mapping: Create a comprehensive mapping document that meticulously details how each field in SFMC will be mapped to corresponding fields in HubSpot. Account for data type differences and potential transformations needed. Pay special attention to custom fields. Thoroughly test this mapping on a small sample of data before proceeding.
2. Data Cleaning and Deduplication: Identify and correct any inconsistencies or duplicates within your SFMC data before migration. This will prevent propagating errors into HubSpot.
3. Business Unit Segmentation: Define clear criteria for separating data according to your business units. This ensures data is organized correctly in HubSpot.
4. Phased Migration Approach: Instead of migrating all data at once, break the process into smaller, manageable batches (e.g., 50,000-100,000 records per batch). This allows for easier error detection and correction and avoids overwhelming the API.
5. Automated Pipeline Development: Develop a custom automated pipeline using a tool like Latenode (or a similar platform) to manage the migration process. This pipeline should handle batch processing, error handling (with rollback capabilities), data transformation, and detailed logging.

Step 2: Migration Execution:

1. Execute the Pipeline: Run the automated pipeline, carefully monitoring its progress. The chosen tool will provide real-time status updates and logging for each batch.
2. Data Validation: After each batch, validate the data in HubSpot to ensure accuracy and completeness. Compare the migrated data against the source data in SFMC.
3. Iterative Refinement: Based on the validation results, adjust the data mapping or pipeline logic as needed. This iterative approach ensures continuous improvement and minimizes errors.

Step 3: Post-Migration Verification:

1. Complete Data Verification: Once all batches are processed, conduct a final comprehensive data verification to ensure that all records have been migrated accurately and completely.
2. Workflow and Automation Testing: Thoroughly test all HubSpot automations and workflows to ensure they function correctly with the migrated data. Address any discrepancies found.
3. Parallel Operation: Run both SFMC and HubSpot in parallel for at least two weeks to identify any unforeseen issues or data discrepancies that may arise.

Common Pitfalls & What to Check Next:

• API Rate Limits: Be aware of and plan for API rate limits from both SFMC and HubSpot to avoid throttling. Adjust batch sizes as needed.
• Data Mapping Errors: Even small mapping errors can lead to significant data issues. Thoroughly review and test your mapping before and during the migration.
• Insufficient Logging: Detailed logging is critical for troubleshooting. Ensure your pipeline generates comprehensive logs to aid in diagnosing and resolving errors.
• Lack of Error Handling: Implement robust error handling and rollback mechanisms to prevent data loss in case of failures.

Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!

[AdventurousHiker17]

yeah, for sure! def break it down into smaller chunks. we did like 50k at a time and it was way smoother. make sure u r also verifying field mappings coz some custom fields just dont align well and can lead to a mess, trust me on that!

[Neo_Movies]

I conducted a similar migration 18 months ago with about 800,000 records. The SFMC to HubSpot integration manages multi-business units well, but it’s crucial to clarify data mapping from the start. We experienced issues because our SFMC segmentation rules didn’t transfer accurately, which disrupted our automated workflows initially. Be sure to audit everything before commencing the transfer—automation, email templates, and so on. Operating both systems in parallel for at least two weeks is advisable. This approach will help you identify any unusual edge cases and data discrepancies that may not be immediately apparent. Our process took three weeks in total, but having that safety net was invaluable.